Poverty Alleviation through Geographic Targeting:
How Much Does Disaggregation Help?
Chris Elbers, Tomoki Fujii, Peter Lanjouw, Berk Özler, and Wesley Yin1
In this paper, using recently completed “poverty maps” for Cambodia, Ecuador, and
Madagascar, we simulate the impact on poverty of transferring an exogenously given budget to
geographically defined sub-groups of the population according to their relative poverty status.
We find large gains from targeting smaller administrative units, such as districts or villages.
However, these gains are still far from the poverty reduction that would be possible had the
planners had access to information on household level income or consumption. Our results
suggest that a useful way forward might be to combine fine geographic targeting using a
poverty map with within-community targeting mechanisms.
Key Words: Targeting, Poverty, Poverty Maps.
JEL Classification Numbers: C15, I32, H53.
World Bank Policy Research Working Paper 3419, October 2004
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the
exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the
presentations are less than fully polished. The papers carry the names of the authors and should be cited
accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the
authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries
they represent. Policy Research Working Papers are available online at http://econ.worldbank.org.
1
Elbers is with Free University, Amsterdam; Fujii is with University of California, Berkeley; Lanjouw
and Özler are with the World Bank; and Yin is with Princeton University. The authors are grateful to
Martin Ravallion, Norbert Schady, and participants in the “Poverty and Applied Microeconomics”
seminar series at the World Bank for useful discussions and comments. Corresponding author: Berk
Özler, World Bank, 1818 H St., NW, Washington, DC 20433, telephone: 202-458-5861, fax: 202-5221153, email: [email protected].
1
2
I. Introduction
Public policies in developing countries are often articulated in terms of poverty
reduction objectives. Resources for such purposes are invariably scarce relative to the number
and magnitude of competing claims.
Spending priorities must be defined, and it is often
desirable to target social transfers to those beneficiaries whose needs are most urgent. Coady
and Morley (2003) survey experience with such targeted transfer programs and show that
errors of inclusion and exclusion are unavoidable consequences of such targeting efforts.
Efforts aimed at improving targeting of public spending generally focus on reducing either one,
or sometimes both, of these types of errors.
Because the precise economic circumstances of households can be difficult to ascertain
it is not easy to define who should be eligible to receive a government transfer. Nor is it
straightforward to design an administrative mechanism to ensure that the transfer actually
reaches the intended beneficiary. In practice governments often exploit geographic variability
in the design of targeting schemes: poverty is typically thought to be more concentrated in
some areas of a country than others and most countries have an administrative structure that
disaggregates to different levels. For example, the central government, located in the capital
city, may rely on state or provincial governments to implement government policies at the state
or province level. These administrations might rely, in turn, on counties or districts, which
may themselves rely on yet lower levels of administration.
Resources aimed at poverty
reduction can thus be directed to those localities where poverty is concentrated and
administration of these transfer schemes can be carried out at the relevant local level.
3
Despite their intuitive appeal, transfer schemes that target poor communities remain
difficult to design. One of the difficulties concerns information on the spatial distribution of
poverty. In many developing countries there is now a good deal of experience with methods to
conceptualize, measure, and analyze poverty. Yet most of the empirical evidence that has been
brought to bear on this topic tends to be at the national level. This is primarily due to the fact
that data on incomes or consumption expenditures, which serve as prime input into the
quantitative analysis of poverty, tend to derive from sample surveys of households. The
sample size of such surveys is often rather small. This implies that statements about the spatial
distribution of poverty can generally refer only to very broad geographic breakdowns. It is not
uncommon, for example, for surveys to be “representative” only for urban and rural areas of a
country, in combination with perhaps a very crude geographical breakdown of the country into
broad regions (north/south or hills/plains).
While there is a general suspicion in many
countries that poverty occurs in geographic “pockets” that are defined fairly precisely, sample
surveys do not readily allow one to confirm or refute such notions.2
Absent detailed information on poverty outcomes at the local level, policymakers
interested in targeting transfers to the local level have often sought to use proxy indicators of
local poverty outcomes. For example, the Microregions program that is being contemplated by
SEDESOL (Ministry of Social Development) in Mexico is proposing to deliver social services
and to promote productive investment projects in a set of municipalities that have been selected
on the basis of a marginality index produced at the municipality level by CONAPO (de Janvry,
Sadoulet, Calva and Solaga, 2003). This index is based on a set of simple variables taken from
the population census, and is believed to be correlated with conventional notions of poverty.
2
However, recent research shows that beliefs regarding the existence of such “pockets of homogeneous poverty” may not always be
true. Elbers et al. (2004) demonstrate that small communities in three developing countries are found to vary markedly from one
4
When such proxy indicators are used for targeting instead of explicit poverty estimates, there is
mis-targeting both due to the targeting errors described above and due to problems with the
proxy welfare index at the community level.3
In recent years there has been growing experience with the development of “poverty
maps” that explicitly estimate consumption or income-based welfare outcomes at the local
level.4 This approach involves imputing consumption or income at the unit-record level into
the population census based on a set of regression models estimated from household survey
data. The imputed household-level income or consumption estimates are then aggregated into
welfare indices (including but not confined to poverty measures) at different levels of
geographic aggregation. While there is inevitably a degree of statistical error associated with
these welfare indices, this uncertainty can be quantified, and it is not uncommon to find that
the method produces reliable estimates of poverty for communities comprised of only 1,0005,000 households on average.5
This paper asks to what extent the high degree of spatial disaggregation offered by such
poverty maps in three different countries can help to improve targeting schemes aimed at
reducing poverty. In this sense, the paper closely builds on the earlier analysis in Ravallion
(1993) who finds that spatial disaggregation to the broad regional level in Indonesia – the
lowest level at which household survey data provide reliable estimates of poverty – improves
targeting but only to a modest extent. As in Ravallion (1993), we consider the distribution of a
hypothetical budget to a country’s population, assuming that we have no information about the
poverty status of this population other than the geographic location of residence and the level
another in terms of the degree of inequality they exhibit and that there should be no presumption that inequality is less severe in poor
communities.
3
For an example of the latter, see Hentschel et al. (2000).
4
Hentschel et al, 2000, and Elbers, Lanjouw and Lanjouw, 2002, 2003 describe the basic methodology. At present, the methodology
has been implemented or is in the process of being implemented in some 30 developing countries. For further details see the website:
5
of poverty in each location. As a benchmark case with which to compare our results, we make
the extreme assumption of no knowledge whatsoever about the spatial distribution of poverty –
in which case our given budget is distributed uniformly to the entire population. We set up a
series of comparisons to this benchmark, where we assume knowledge about poverty levels for
administrative units with progressively smaller populations.
For a given level of
disaggregation, we ask how knowledge about poverty outcomes across regions can be
incorporated into the design of a transfer scheme so as to improve the overall targeting
performance relative to the benchmark case. We consider a variety of transfer schemes that
make use of this knowledge in different ways. The schemes range from simple, intuitive
transfer schemes to more sophisticated ones, in the latter of which expected poverty at the
national level is minimized given the information and budget constraints. We are interested in
comparing performance across schemes, as well as the relative performance of each scheme at
alternative levels of disaggregation. We consider performance in terms of the squared poverty
gap – a measure of poverty that increases not only with the number of people below the
poverty line but is also particularly sensitive to the distance between a poor person’s income
level and the poverty line. To avoid making strong assumptions about the exact value of the
poverty line or the size of the budget, we also show results for two poverty lines and two
hypothetical budgets.6
Finally, we report how close “optimal geographic targeting” in
combination with our poverty maps, comes in terms of poverty reduction to the hypothetical
scenario of “perfect targeting”. From this we can get a sense of the potential benefit from
combining detailed geographic targeting with additional targeting mechanisms such as
http://econ.worldbank.org/programs/2473/topic/14460/.
5
For a more detailed discussion, see Demombynes et al. (2002).
6
We have tried more poverty lines and budget sizes, but do not present them in this paper for brevity. For a formal discussion of
using “program dominance curves” to assess the poverty impact of different programs, see Duclos, Makdissi, and Wodon (2003).
6
individual or household level means-testing or the incorporation of self-selection targeting
mechanisms within communities.
Our simulations are carried out using recently produced poverty maps for Ecuador,
Madagascar and Cambodia. These countries are obviously highly heterogeneous in terms of
their geographic location, but have also very different social and political structures and are at
different stages of overall development. We are interested to examine whether there exist
commonalities across these countries in terms of the degree to which the availability of poverty
data at the local level can contribute to improvements in the targeting of public resources.
We find that there are potentially large gains to targeting performance from
disaggregating to the local level. The benefits become increasingly evident as one makes use
of more and more disaggregated data on poverty. In all three countries examined, we show that
relative to a uniform transfer the same impact on poverty can be achieved at considerably less
expense when targeting is based on the highly disaggregated poverty estimates that are
available in poverty maps. The gains are generally more muted when the targeting scheme
makes only crude use of the local level poverty estimates. They are also lower if the poverty
line in a given country is particularly high, or if the budget available for transfer is particularly
large. In all countries we find as well that despite the gains from geographic targeting, our
inability to target households directly implies that overall targeting performance remains far
from perfect. This implies that there may be scope for combining geographic targeting with
other targeting methods in order to reduce errors of inclusion and exclusion even further.
The results in this paper are likely to be of use to policy makers interested in the design
of transfer schemes aimed at reducing poverty. It is important to emphasize, however, that
there are important caveats that attach to these results. First, we assume that the willingness of
7
government to consider geographic targeting implies a willingness to sacrifice horizontal
equality in favor of improved targeting efficiency. In other words, the government is willing to
accept that households with equal pre-transfer per-capita consumption levels might enjoy
different post-transfer consumption levels. Second, we assume in this paper that the budget
available for distribution is exogenously determined. We abstract away entirely from the
question of how the transfers are to be financed. As has been argued by Gelbach and Pritchett
(2002), political economy considerations are likely to influence options for resource
mobilization. It is possible, for example, that fine geographic targeting of transfers is less
appealing to voters than a uniform transfer scheme – and this could translate into lower overall
budgets available for such targeted transfers than would be the case if the transfer scheme were
uniform. Third, we do not address the very real possibility that the costs of administering a
given transfer scheme may increase with the degree of disaggregation. It is quite possible that
each unit of administration incurs some fixed costs in terms of staff, equipment, and so on, in
order to implement a given transfer scheme. Relying on ever lower levels of government to
administer the transfer scheme could raise overall administration costs, thereby reducing the
overall amount left to transfer. Fourth, it is well recognized that the availability of transfers to
certain groups in the population may induce behavioral responses in the population. Those
considered ineligible for the transfer might behave in such a way as to appear eligible.
Households might, for example, move to those locations where it is announced that transfers
will be directed or they can pretend that they are residents of those locations while living
elsewhere. It is possible that the cost, to households, of such migration or cheating is lower
when it involves small neighboring communities as opposed to large neighboring regions.
8
Finally, we have not examined optimal targeting in the context of local politicaleconomy considerations or in the presence of community specific public goods as a result of
voluntary contributions at the local level. To the extent that the inequality level in a given
locality represents some kind of political economy “equilibrium”, it is not clear how a
government transfer to a community will actually get distributed across the population in the
community. The inequalities in power and influence that prevail in the community are likely
to influence how such transfers are allocated.
Such factors are likely to result an
overestimation of the impact of the targeting scheme on poverty reduction.7 In addition,
Dasgupta & Kanbur (2003) shows that basic results of the targeting literature can change in the
presence of community-specific public goods, and that optimal targeting for poverty alleviation
can lead to paradoxical results for certain values of the poverty aversion parameter, for
example that targeting transfers to the richer community can result in greater welfare gains for
the poor (via the increased provision of public goods by the richer segments).
As a result of these caveats, it is important to stress that the gains from fine geographic
targeting illustrated in this paper should be viewed as illustrative only. These potential gains
should be juxtaposed against the potential costs of such targeting and political-economy
considerations.
Policymakers need to assess such programs on a case-by-case basis to
determine whether fine-geographic targeting is the appropriate strategy.
Our paper is structured as follows. In the next section we briefly summarize the
methodology and data underpinning the poverty map estimates in Ecuador, Madagascar and
Cambodia. We emphasize that the spatially disaggregated poverty data available to us are
estimates, with confidence bounds, rather than actual measures of poverty. We indicate how
7
On the other hand, it is also possible that the infusion of transfers into a poor community would increase risk-sharing in that
community and thereby contribute to further reductions in poverty.
9
we incorporate this imprecision into our simulation analysis. Section III describes the different
targeting schemes that are assessed in the simulation stage. In this section we also demonstrate
how one particular targeting scheme can be viewed as optimal in terms of ensuring the
maximum possible gains from geographic targeting. Section IV presents the results from our
simulation analysis, and Section V presents a concluding discussion.
II.
Producing Local Estimates of Poverty
The methodology we implement here has been described in detail in Elbers, Lanjouw
and Lanjouw (2002, 2003). We estimate poverty based on a household per-capita measure of
consumption expenditure, yh.
A model of yh is estimated using household survey data,
restricting explanatory variables to those that are also found in, and strictly comparable to, the
population census.
The regression models consumption on a set of household-level
demographic, occupational and educational variables as well as census variables calculated at
the level of the census-tract or other level aggregation above the household level.8
Letting W represent an indicator of poverty or inequality, we estimate the expected
level of W given the observable characteristics in the population census and parameter
estimates from model estimated on the household survey data.
We model the observed log per-capita expenditure for household h as:
ln yh = xhβ+ uh
(1)
where xhβ is a vector of k parameters and uh is a disturbance term satisfying E[uh|xh] = 0. The
model in (1) is estimated using the survey data. We use these estimates to calculate the welfare
of an area or group in the population census. We refer to our target population as a ‘region’.
10
Because the disturbances for households in the target population are always unknown,
we consider estimating the expected value of the indicator given the census households’
observable characteristics and the model of expenditure in (1). We denote this expectation as
µv = E[W | Xv, ξ ]
(2)
where ξ is the vector of model parameters, including those that describe the distribution of the
disturbances.
In constructing an estimator of µv we replace the unknown vector ξ with consistent
estimators, ξ̂ , from the survey-based consumption regression.
This yields µ̂v . This
expectation is generally analytically intractable so we use simulation to obtain our estimator,
µ~ν .
The difference between µ~ν , our estimator of the expected value of W for the region,
and the actual level of welfare for the region reflects three components.
The first,
(idiosyncratic error), is due to the presence of a disturbance term in the first stage model which
implies that households’ actual expenditures deviate from their expected values.
This
component becomes increasingly important as the target population becomes very small. The
second component of our prediction error is due to variance in the first-stage estimates of the
parameters of the expenditure model (model error). We calculate the variance due to model
error using the delta method (see Elbers et al 2002, 2003). The third component of our
prediction error is due to inexact method to compute µ̂ (computation error). This component
can be set arbitrarily small by choosing a large enough set of simulation draws.
8
In the case of Madagascar and Cambodia, we also include regressors from tertiary datasets in the regression model (see Mistiaen,
Ozler, Razafimanantena, Razafindravonona, 2002, and Fujii, 2003),
11
Implementation
The first-stage estimation is carried out using household survey data in our three
respective countries. These surveys are stratified at the region or state level, as well as for rural
and urban areas.
Within each region there are further levels of stratification, and also
clustering. At the final level, a small number of households (a cluster) are randomly selected
from a census enumeration area.
Our empirical model of household consumption allows for an intra-cluster correlation
in the disturbances (see Elbers, Lanjouw and Lanjouw, 2002, 2003 for more details). Failing to
take account of spatial correlation in the disturbances would result in underestimated standard
errors. We estimate different models for each region and we include in our specification census
mean variables and other aggregate level variables cluster-level effects.
All regressions are
estimated with household weights. We also model heteroskedasticity in the household-specific
part of the residual, limiting the number of explanatory variables to be cautious about
overfitting. We approximate both the cluster- and household-level disturbances as either
normal or t distributions with varying degrees of freedom.9 Before proceeding to simulation,
the estimated variance-covariance matrix is used to obtain GLS estimates of the first-stage
parameters and their variance.
Data
The data used in this study consists of a household survey and a population census from
each of Ecuador, Madagascar, and Cambodia. Table 1 presents the basics on each of the data
9
Rather than drawing from parametric distributions in our simulations, we can also employ a semi-parametric approach by drawing
from observed residuals in the first stage model. Our results have generally been found to be quite robust to the choice of parametric
or semi-parametric draws.
12
sources, such as year, sample size, stratification, etc. For more detail on the data, please refer
to the studies listed in the “References” row in Table 1.
III.
Transfer Schemes and Simulation Procedures
The transfer schemes
As described in Section I, we compare a variety of targeting schemes against a benchmark
scheme which assumes absolutely no knowledge of the geographic distribution of poverty.
One of our main objectives is to see whether, and to what extent the availability of poverty
estimates for different geographic locations can help to improve the poverty impact of
distributing a given budget. In our baseline, benchmark, case we postulate that the government
has a budget, S, available for distribution and wishes to transfer this budget in such a way as to
reduce poverty. However, because the government is assumed to have no knowledge of who
the poor are or where they are located, it is unable to distribute its budget in any manner other
than a lump-sum transfer to the entire population of size N. In our benchmark case, thus, we
calculate the impact of transferring S/N to the entire population. 10
Optimal use of geographically disaggregated information on poverty has been
investigated by Kanbur (1987), Ravallion and Chao (1988), Glewwe (1991), Ravallion (1993),
and Baker & Grosh (1994). Kanbur (1987) formalized the theoretical problem of policy design
under imperfect information, while Ravallion & Chao (1998) demonstrated how this general
targeting problem can be solved in a computationally feasible way.11 For our purposes, the
10
It could be argued that our benchmark scenario is not terribly realistic. Perhaps more likely would be a situation where absence of
detailed information on the extent and distribution of poverty, and absent any specific effort to target the poor, would result in a
default situation of resources being appropriated by the non-poor (see the discussion in Campante and Ferreira, 2003). To the extent
that this is true our estimates of the gains from targeting, once we assume some information on the distribution of poverty, might be
seen as conservative estimates of the true benefits.
11
As we use predicted expenditures from census data unlike Ravallion and Chao (1988), who use observed income data from
household surveys, we utilize a different algorithm to solve the optimization problem. Applying their algorithm to our setting would
yield the same results.
13
important result from Kanbur (1987) is that if decision makers wish to transfer resources in
such a way as to minimize poverty summarized by the Foster-Greer-Thorbecke (FGT) class of
poverty measures with parameter value α>1, then on the margin the group with the higher
FGTα-1 should be targeted. In other words, if the government wishes to minimize the squared
poverty gap (equal to a poverty measure from the FGT class with α=2), then geographic
regions should be ranked by the poverty gap (FGT with α=1) and lump-sum transfers made
until the poverty gap of the poorest region becomes equal to that in the next poorest region, and
then transfers to these two regions should be continued until their poverty gap is equal to the
next poorest region, and so on, until the budget is exhausted.
Let ych denote the per capita expenditure of household h (with m members) living in
group c. Assume that the government is able to provide lump-sum supports ac that differ
across groups c. Thus, after-support expenditures are ych + ac. Suppose the government wishes
to minimize expected FGT2 after transfers subject to the constraint that total transfers are
limited by the budget S:
min = ∑∑ mch Avg[( ych( r ) + ac − z ) 21( ych( r ) + ac ≤ z )]
ac
c
(3)
h
subject to
∑a ∑m
c
c
ch
≤S
(4)
h
ac ≥ 0
The rth simulated expenditure of household h in c is indicated by ych(r ) . The (linear) Avg[ ]
operator indicates taking the average over simulations r=1,….,R (see below). 1(x) is an
indicator function, 1(TRUE)=1, 1(FALSE)=0.
Standard applications of the Kuhn-Tucker
conditions leads to first-order conditions for optimal ac:
14
∑m
ch
h
Avg[( z − y ch( r ) − a c )1( y ch( r ) + a c ≤ z )] ≤ λ ∑ mch
h
(5)

∑h mch Avg[( z − y ch( r ) − ac )1( y ch( r ) + ac ≤ z )] 

ac  λ −
=0
∑h mch




along with the earlier constraints on ac. Note that
∑m
ch
h
Avg[( z − ych( r ) − ac )1( ych( r ) + ac ≤ z )]
(6)
∑m
ch
h
is nothing but the average simulated after-transfer FGT1 for group c. In other words, in the
optimum only the groups with the (after-transfer) highest predicted FGT1 get expenditure
support, and those that do receive transfers get the amount which equalizes predicted FGT1.
The second targeting scheme that we compare against our benchmark case assumes some
knowledge of the spatial distribution of poverty, but does not make use of this knowledge in
any particularly scientific or systematic way. This “naïve” targeting scheme was selected in
order to contrast with the “optimal” scheme described above. There are reasons to believe that
implementation of an “optimal” scheme will be difficult in practice. It is often important for
governments to be able to communicate in a very clear and simple way how resources will be
targeted, and this need for transparency and ease of communication may prevent governments
from carrying out the fine-tuning needed for an optimal scheme.
Of course, there are virtually an infinity of “naïve” schemes that could be implemented.
The scheme implemented here is one, particularly straightforward, example.
We have
experimented with a variety of alternative, more complicated, versions of this naïve scheme.
We have not found any alternative that is obviously more effective. Indeed, the specific
15
scheme implemented here has the virtue of not only being simple but also surprisingly
effective at times.
Our “naïve” scheme takes the following form. We first rank geographic areas by estimated
poverty. If our interest was to gauge the impact of our scheme on the headcount rate, we
would rank areas by the headcount. But as we wish, in this paper, to assess the impact on the
squared poverty gap we rank by those estimates. We have an assessment of overall poverty in
the country. We take our budget S and divide it by the total number of poor persons in the
country, Np. Our budget divided by the total number of poor persons yields the transfer a that
will be distributed to each person. We select the poorest geographic area and transfer a to all
persons in that area. If the budget has not been exhausted in the first region, we move to the
next poorest region and transfer a to all persons in this second region. We continue until the
budget is exhausted. In the marginal region - that in which the budget is exhausted - we do not
transfer a but transfer an equal share of whatever remains in the budget to the population of
that last region. Note that this scheme does not guarantee some amount of transfer to all
regions.
The scheme also implies that households will be receiving differing amounts
according to their overall size. 12
Implementation
Operationalizing our simulation exercises involves taking outputs from the micro-level
estimation procedure described in Section II and subjecting these to the simulation procedures
described above.
To recap, in Section II we described a procedure whereby per-capita
12
Two alternatives that we have experimented with (but do not report here as results were not noticeably better) include a “naïve
poverty-gap” scheme and a “naïve poverty share scheme”. In the former we rank communities by estimated poverty. We then
cumulate the poverty gaps amongst the poor within each community. We divide that total by the number of poor people in the region.
We then transfer this amount to everyone in the community (poor and non-poor). We carry on until the budget is exhausted, and
estimate impact on aggregate poverty. In the latter scheme we calculate community i’s contribution to total poverty. We transfer that
16
consumption is predicted at the level of each household in the population census, based on a
consumption model estimated in the household survey.
However, because predicted
household-level per capita consumption in the census is a function not only of the parameter
estimates from the consumption model estimated in the survey, but also of the precision of
these estimates and of those parameters describing the disturbance terms in our consumption
model, we do not produce just one predicted consumption level per household in the census.
Rather, r predicted expenditures are produced for each household (in our three countries, we
have carried out one hundred predictions). For each respective r, parameter estimates are
drawn from a multivariate normal distribution that respects the variance-covariance matrices
estimated in the survey-based consumption and heteroskedasticity regressions. In addition,
disturbance terms at the cluster and household level are drawn from their respective parametric
or semi-parametric distributions. These draws are then applied to the census-level regressors
and per-capita consumption is predicted.
For the next r, a new set of parameters and
disturbances are drawn and a new per-capita consumption measure is predicted. The resulting
“dump-file” of r predicted expenditures for every single household in the population census is
the key database underpinning “poverty maps” and the policy-simulation exercise explored
here.13
Simulating the impact of uniform targeting
Our baseline, benchmark, policy simulation is calculated in the following way. Budget S is
divided by total population N. The resulting transfer a is added to each predicted expenditure
fraction of the budget uniformly to each household in the community. Note that this latter scheme ensures that all communities
receive at least something.
13
The poverty map estimate of poverty in community, province or region c is produced from this dump file in the following manner:
for every replication r, poverty is estimated over all households in c (after weighting by household size). The average of all poverty
17
in the “dump file”, to yield ych(r ) +a. For each replication r we estimate post-transfer national
poverty. The average across the r replications of the estimated post-transfer poverty rates
yields our expected poverty rate associated with the benchmark, untargeted lump-sum transfer
scheme. This new estimated poverty rate can be compared to the original national-level
poverty estimate from the poverty map to gauge the impact of the transfer.
Simulating the impact of “optimal” geographic targeting
Simulating the impact of the “optimal” targeting scheme is a bit more complicated. We
want to equalize the following expression across the poorest locations of a country:
z
Gc (a c ) = ∫ ( z − y − a c ) + dFc ( y )
0
(7)
which is z times the poverty gap in location c , after every person in the location has received a
transfer ac. Fc(y) is the average of the R simulated expenditure distributions of c. The function
(x)+ gives the ‘positive part’ of its argument, i.e. (x)+=x, if x is positive, otherwise 0. Transfers
ac (which must be nonnegative) add up to a given budget S:
∑N a
c
c
=S
(8)
c
where Nc is the population size of location c. After transfers there is a group of locations all
sharing the same (maximum) poverty gap rate in the country. These are the only locations
receiving transfers.
We solve this problem by first solving a slightly different problem. Consider the
minimum budget S(G) needed to bring down all locations’ poverty gaps to at most the level
G/z. This amounts to transferring an amount ac (G) to locations with before-transfer poverty
estimates, over the r replications, yields the estimated poverty rate in community c, and the standard deviation yields the associated
estimated standard error.
18
gaps above G/z, such that Gc (ac (G )) = G . Once we know how to compute S(G), we simply
adjust G until S(G) equals the originally given budget for transfers S. To implement this
scheme we must solve the following equation for ac:
G=
z
∫ (z − y − a )
c
0
+
(9)
dFc ( y )
In what follows we drop the location index c for ease of notation. Using integration by parts it
can be shown that
z
G (a ) = ∫ ( z − y − a ) + dF ( y ) = ∫
0
z −a
0
F ( y )dy
(10)
In other words we need to compute the surface under the expenditure distribution between
expenditure levels y=0 and y=z-t, for values of t up to z. Instead of computing G(t) exactly, we
use a simple approximation. For this to work we split the interval [0,z] in n equal segments and
assume that the ‘poverty mapping’ software has generated expected headcounts for poverty
lines z k/n, where k=0, …,n. In other words we have a table of F(z k/n). Using the table we
approximate F(y) by linear interpolation for y between table values. With the approximated
expenditure distribution it is easy to solve for transfers as a function of G (see below). In
practice we find that n=20 gives sufficiently precise results.14
The computational set-up is as follows (note that the numbering we adopt means going
from z in the direction of 0 rather than the other way around). Define b0=0, and for k=1,...,n,
bk as the surface under the (approximated) expenditure distribution between z-kz/n and z-(k1)z/n, divided by z:
bk =
1
(F ( z − kz / n) + F ( z − (k − 1) z / n))
2n
(11)
14
Other interpolation schemes are possible. For instance, if the poverty gap is given at table values zk/n an even simpler computation
presents itself. Often the poverty mapping software will give percentiles of the expenditure distribution. These can also be used for
interpolation, but the formulas are more cumbersome, since the percentiles are not equally spaced.
19
Let g0 be the original poverty gap, or in terms of the discussion above, g0=G(0)/z. Fork=1,...n,
put
g k = g k −1 − bk
(12)
The gk are the poverty gaps of the approximated expenditure distribution for successively
lower poverty lines z-kz/n. Let ak be the per capita transfer needed to bring down the poverty
line to z-kz/n:
a k = kz / n
(13)
We can now solve for per capita transfers as a function of the intended poverty gap g<g0:
1. Find k such that g k +1 ≤ g < g k .
2. The per capita transfers resulting in poverty gap g are
a( g ) = a k +
gk − g z
⋅
g k − g k +1 n
(14)
This scheme can be implemented using standard spreadsheet software.
Simulating the impact of “naïve” geographic targeting
Simulating the impact of our “naïve” transfer scheme on the basis of the “dump-file”
described above is far more straightforward. We take our poverty map as the basic statement
on the distribution of poverty in the country.
On the basis of the poverty map we identify the
localities that will receive priority in the targeting scheme (we consider initially regions, then
provinces, then communities, etc.). We calculate the amount a that will be targeted to all
persons in the priority regions (budget S divided by the total number of poor people in the
country, Np). We simulate the targeting scheme in turn for each replication r by allocating a to
all persons in our priority regions (irrespective of whether, in replication r, those regions are
particularly poor or not) until the budget is exhausted. We re-calculate the post-transfer
20
national poverty rate in replication r. The average post-transfer national poverty rate across all
replications provides our estimate of how poverty will have changed as a result of the transfer
scheme. This expected poverty rate can be compared to the original estimate of national
poverty from the poverty map, and to the estimate of the poverty associated with an untargeted
lump-sum transfer.
Calculating “Equivalent Gains”
In thinking about the “performance” of the transfer scheme we are interested not only in
the poverty impact of a specific scheme, but also in how much more “expensive” a given
poverty reduction is without targeting as opposed to with geographic targeting. To explore the
latter we apply a variant on the simulation procedures described above whereby we calculate
how much smaller the overall budget S can be in order to achieve the same poverty impact with
optimal targeting as with the untargeted uniform lump-sum transfer.
Distance from Perfect Targeting
As has been emphasized in section II, the poverty map cannot provide reliable estimates of
poverty below some level of aggregation. This is because the idiosyncratic component of the
overall standard error on the poverty estimate becomes progressively larger as poverty is being
measured over progressively smaller groups. For policy makers therefore, the poverty map
cannot be viewed as a tool to assist with the identification of, say, poor households or small
groups of households. A rough rule of thumb is that poverty map estimates become unreliable
for communities of less than 1000-5000 households.
21
It remains of interest, however, to ask how much of a further reduction in poverty could be
expected if, rather than being limited to geographic targeting of communities of 1000-5000
households, policymakers could actually target individual households.15
The “dump-file”
underpinning the policy simulations described above can be drawn on to shed light on this
question. While this file cannot provide a reliable listing of poor households, it is possible to
simulate the change in poverty in a given locality associated with household-level targeting on
the basis of exactly the same optimal transfer scheme procedure described above (but where
each household h now corresponds to a separate community c). In the discussion of results
below we provide an assessment of the distance (in terms of poverty reduction) between
feasible geographic targeting (given the poverty map) and the ideal of perfect household-level
targeting. To the extent that this distance is large we can provide a sense of the potential
benefit of combining geographic targeting with additional complementary targeting efforts.
Budgets and Poverty Lines
Before turning to a discussion of results, we describe briefly our selection of budgets, S,
and our poverty lines. As has been mentioned in Section I, we assume that the budget
available for distribution has been exogenously set. As is intuitively clear, the potential
benefits from targeting will vary with the overall size of budget, and for this reason we conduct
the simulation analyses described above for two different budget sizes. In each of the three
countries examined here we identify the per capita consumption value of the 25th percentile of
15
Relatedly, one could ask how much of a further benefit could one expect if, rather than being compelled to provide only lump-sum
transfers to poor communities, policymakers were able to combine geographic targeting with, say, means testing within poor
communities.
22
the consumption distribution.16 We scale this consumption value by the total population. Our
benchmark budget is set to equal 5% of this total value, and we experiment with a higher
budget of 10% of this figure as well.
Gains from targeting also vary with the choice of poverty line. In this study, we select
as benchmark poverty line, that line which yields a 20% headcount rate in each of our three
countries.17 For comparison we experiment as well with a higher poverty line corresponding to
a headcount rate of 40%.
IV.
Results
Optimal targeting
Tables 2a-2c present the basic results from our simulations with the optimal targeting
scheme in Ecuador, Madagascar and Cambodia. There are five main observations. First, in all
three countries, the availability of disaggregated data on poverty can help to improve on a
uniform lump-sum transfer across the entire population. Targeting transfers to poor localities,
in accordance with the optimization scheme outlined above, yields lower values of the national
FGT2 than when the budget is transferred as a uniform lump-sum transfer to the entire
population. Second, the more disaggregated the poverty map, the greater the improvement
over the uniform lump-sum transfer. Traditional household surveys are generally able, at best,
to provide estimates of poverty at the first administrative level. The simulations here suggest
that with estimates of poverty at the 2nd or 3rd administrative level, further improvements in
terms of impact on the FGT2 with a given budget are attainable, and are non-negligible. Third,
16
The consumption distribution is constructed on the basis of the average, across r replications, of household-level predicted percapita consumption in the population census.
17
Within each replication r, the predicted per-capita consumption level associated with a 20% headcount rate is identified. The
average across the r replications of this predicted consumption level is then taken as poverty line. It is clear that this poverty line will
23
the gains from spatial disaggregation are attenuated the higher the budget and poverty line. In
all three countries examined, the benefits from geographic targeting are most pronounced in
our base case with the lowest budget and lowest poverty line. Fourth, while the general
patterns we observe are similar across our three case-study countries, they are not identical. In
particular, in Ecuador, where our focus is only on rural areas, the gains in general from
targeting at the local level are somewhat more muted than in Madagascar and Cambodia.
Fifth, even though our base-case low budget represents a considerable resource envelope (as
evidenced by the sizable impact on poverty of even a uniform lump-sum transfer) it is clear
that optimal targeting at the lowest possible level of disaggregation is far from sufficient to
eliminate poverty altogether (see further below).
Table 3 considers, for rural Ecuador, the statistical precision of comparisons of poverty
based on our alternative targeting scenarios. While the point estimates on the FGT2 measure
generally take very low values, differences in estimated poverty across scenarios remain
strongly significant (Table 3). To ascertain the statistical significance of poverty differences
we return to the optimal transfer simulations and estimate not FGT2 values, but rather the
difference in the estimated FGT2 based on optimal targeting at the parroquia level vis-à-vis
targeting at the uniform, region, province, and cantonal level. As we can see in Table 3 not
only does targeting at the parroquia level yield the lowest level of the FGT2 across our four
budget/poverty line scenarios and our different levels of geographic disaggregation, but this
estimate is also lower in a statistical sense. We can see that, in general, the estimated
difference in point estimates is roughly 7-10 times the value of the standard error of that
difference, even when we compare canton-level targeting against parroquia-level targeting.
not necessarily yield a 20% headcount rate within each replication, nor would it yield such a rate for average per capita consumption
at the household level (averaging across r replications).
24
Tables 4a-4c present the findings in Tables 2a-2c from a different perspective. We ask
now how much more cheaply one could have achieved the same impact on poverty as with the
uniform lump-sum transfer, if use had been made of a poverty map in an optimal fashion. We
can see, for example, in Table 4a that in rural Ecuador even a poverty map at the region level
(the level of spatial disaggregation that is representative in the household survey) would have
permitted the same reduction in the FGT2 as the uniform transfer (in the low budget, low
poverty line base case) at only 83% of the cost of the uniform transfer. With a more detailed
poverty map that allows for disaggregation down to the parroquia level, the same impact could
have been achieved at only 58% of the cost of the uniform transfer. In Madagascar, and
Cambodia the savings are even more striking. For example, in these two countries one would
need, respectively, only 37% and 31% of the uniform transfer budget to achieve the same
reduction in the FGT2 with optimal targeting at the firaisana and commune-level (Tables 4b
and 4c).
“Naïve” targeting
The optimization scheme implemented above is intuitively straightforward.
But
working out exactly how much to give to communities is not always easy to describe. Given
that the design and implementation of targeting schemes is often part of a political process, and
that there is generally a need to be able to explain allocations in a simple and clear manner, it is
of some interest to ask whether gains from spatially disaggregated geographic targeting are
also significant when the poverty map is combined with simplistic, non-optimal, transfer
schemes. We describe in Tables 5a-5c the results in our three countries of transferring our
given budget on the basis of one such naive and simplistic scheme
It is striking that in all
25
three countries, the reduction in the FGT2 achievable with a naïve scheme, in combination
with the most disaggregated poverty map, is fairly sizeable. Broadly, the reduction in the
FGT2 on the basis of this scheme is similar to the impact with the optimal scheme at one level
of aggregation higher. For example, in Ecuador, combining the parroquia-level poverty map
with our naïve scheme (in the base case of a low budget and low poverty line) yields an
estimated national level FGT2 of 0.0178, marginally higher than the 0.0177 attainable with a
canton-level poverty map and an optimal targeting scheme.
Similarly, in Madagascar,
firaisana-level targeting with the naïve scheme yields a national FGT2 estimate of 0.0135
which can be compared to the FGT2 of 0.0138 achievable with the fivondrona-level poverty
map and optimal targeting. Again, in Cambodia, commune level targeting with the naïve
scheme yields an estimated FGT2 of 0.010 which is equal to that achievable with optimal
targeting at the district level.
The performance of the naïve scheme becomes much less appealing when combined
with a higher budget and higher poverty line. Indeed, there are cases where the impact on the
FGT2 of targeting with this naïve scheme is less pronounced than with a non-targeted uniform
lump-sum transfer (see for example column 2 in Tables 5a-5c and Figures 1 and 2).
Perfect targeting
How well does optimal geographic targeting at the lowest level of spatial
disaggregation permitted by our poverty maps compare to the hypothetical case where we have
information on the poverty status of every individual household in the country? We can
answer this question in a straightforward manner by noting that the cost of eliminating poverty
under the assumption of perfect targeting (i.e. it is possible to observe the precise welfare level
26
of every household and to tailor the transfer received by each household perfectly) is provided
by the FGT1 measure of poverty (weighted by the poverty line and the total population). Thus,
we can calculate from our poverty mapping database the hypothetical cost of eliminating
poverty if it were possible to target the poor perfectly (and there were no behavioral
responses). We can then take this budget and target it, instead, geographically, at the lowest
level of geographic disaggregation that we feel that the poverty map can support. How far are
we from having eliminated poverty when our transfer occurs at this geographic level rather
than having been tailored to the precise circumstances of each poor household? In rural
Ecuador, on the basis of the lower poverty line, optimal parroquia-level geographic targeting of
this budget reduces the FGT2 from 0.028 to 0.0177, only a 37 percentage point decline. At the
higher poverty line, optimal geographic targeting of the budget that, in principle, could
eliminate poverty altogether, reduces the FGT2 from 0.070 to 0.032, a 54 percentage point
decline. Very similar results obtain in Madagascar and Cambodia (for example, see Table 6
for Cambodia).
Why does optimal geographic targeting on the basis of a detailed poverty map fall so
far short of the ideal? In a companion paper, Elbers et al (2004) analyze evidence on the level
and variation of inequality within poor communities. They show that in three countries,
including two of the countries examined here (Ecuador and Madagascar), within-community
inequality varies widely across communities. Some communities exhibit levels of inequality as
high, or higher, than at the national level, while others are significantly more equal. An
important conclusion from this study is that there should be no presumption of lower levels of
inequality in poor communities. In fact, in the three countries studied by Elbers et al (2004),
median inequality is highest amongst the bottom quintile of communities (ranked in terms of
27
average per capita consumption, or in terms of the headcount rate of poverty) and this quintile
also displays the highest degree of variation of inequality across communities. The implication
of this finding is that within poor communities, even small ones with populations of 5000
households or less, there are likely to be both poor and non-poor households. Community level
targeting that transfers a uniform amount to all individuals within these small communities is
thus likely to continue to suffer from leakage. The poverty impact of such targeting will thus
fall short of what would have been possible if perfect targeting were available.
V. Discussion
The recent literature on micro-estimation of welfare based on combined survey and
census data offers a promising avenue for analyzing the potential poverty impact from a variety
of policy proposals, such as geographically targeted transfer schemes. In this paper we have
taken recently completed “poverty maps” for three countries, Ecuador, Madagascar and
Cambodia, and have explored the extent to which the availability of local estimates of poverty
can help to strengthen targeting performance. We have taken the raw census-level output files
from the poverty mapping methodology in these three countries and have simulated the impact
on poverty of transferring an exogenously given budget to geographically defined sub-groups
of the population according to their relative poverty status. We have asked to what extent
effectiveness of targeting in reducing poverty improves as we define these sub-groups at
progressively lower levels of spatial disaggregation.
We have found large gains from targeting smaller administrative units, such as districts
or villages. We have shown that the largest gains from geographic targeting occur when the
overall budget available for transfer is relatively modest, and when the poverty line is not so
28
high as to classify most of the population as poor. We have shown further that the benefits
from targeting are most clearly discerned when expressed in terms of budgetary savings of
achieving a given rate of poverty reduction.
We have noted, however, that despite the
availability of reasonably precise estimates of poverty at the level of communities with
populations of perhaps only 5,000 households, transfer schemes targeting such communities
are still unable to achieve the kind of success that would be attainable if household-level
income or consumption data were available. We suggest therefore that a potentially useful way
forward is to combine fine geographic targeting on the basis of a poverty map with withincommunity targeting based on either self-selection or alternative targeting mechanisms.
Our assessment of targeting performance has been based on an optimal use of estimates
from poverty maps. There might be grounds for concern that the design of transfer schemes
based on such optimized routines suffers from lack of transparency and would be difficult to
describe in simple terms. We have considered, therefore, an alternative transfer scheme, based
on a naïve, non-optimal use of the poverty map. We have found that while this naïve scheme
does not achieve the same success in our three countries as the optimal targeting scheme, its
performance remains surprisingly good in the case where the overall budget and poverty line
are both relatively low. On the other hand, when the budget and/or poverty line is relatively
high, the naïve scheme we implemented can perform less effectively than even a uniform
lump-sum transfer.
To the extent that policy makers are concerned about issues of
communication and transparency, our results suggest that there are conditions under which
even simplistic targeting schemes based on the poverty map can yield encouraging results.
We would be remiss not to restate the important caveats that attach to these results.
First, we have assumed in this paper that the budget available for distribution is exogenously
29
determined. We abstract away entirely from the question of how the transfers are to be
financed. Yet the design of targeting schemes may influence the mobilization of resources.
Second, we have not addressed the possibility of variations in the costs of administering
transfer schemes with the degree of disaggregation. Third, we have not taken into account the
possibility that behavioral responses might vary with the degree of disaggregation in the
transfer scheme. Finally, we have not examined optimal targeting in the context of local
political-economy considerations or in the presence of community specific public goods. The
results in this paper should thus be viewed as suggestive only, and should be combined with
detailed assessments of these mitigating factors.
30
References
Baker, J., and Grosh, M., 1994. Poverty Reduction Through Geographic Targeting: How Well
Does It Work? World Development 22(7): 983-995.
Coady, D. and Morley, S., 2003. From Social Assistance to Social Development: Targeted
Education Subsidies in Developing Countries (Center for Global Development and
International Food Policy Research Institute).
Dasgupta, I., and Kanbur, R., 2003. Community and Anti-Poverty Targeting. Unpublished
manuscript.
De Janvry, A., Sadoulet, E., Calva, L.F., and Solaga, I., 2003. Concept Paper and Terms of
Reference for the External Evaluation of SEDESOL’s Microregions Program. Unpublished
manuscript, Dept. of Agriculture and Resource Economics, UC Berkeley.
Demombynes, G., Elbers, C., Lanjouw, J.O., Lanjouw, P., Mistiaen, J. and Özler, B., 2002.
‘Producing an Improved Geographic Profile of Poverty: Methodology and Evidence from
Three Developing Countries’ in van der Hoeven, R. and Shorrocks, A. (eds) Growth,
Inequality and Poverty: Prospects for Pro-Poor Economic Development (Oxford: Oxford
University Press).
Duclos, J., Makdissi, P., and Wodon, Q., 2003. Poverty-Efficient Transfer Programs: the Role
of Targeting and allocation Rules. CIRPÉE Working Paper 03-05.
Elbers, C., Lanjouw, J.O. and Lanjouw, P., 2002. Micro-Level Estimation of Welfare. Policy
Research Working Paper 2911, Development Research Group, the World Bank, Washington
D.C.
Elbers, C., Lanjouw, J.O. and Lanjouw, P., 2003. Micro-Level Estimation of Poverty and
Inequality. Econometrica 71(1): 355-64.
Elbers, C., Lanjouw, P., Mistiaen, J., Özler, B., and Simler, K., 2004. Unequal Inequality of
Poor Communities. World Bank Economic Review (forthcoming).
Fujii, T., 2003. Commune-level Estimation of Poverty Measures and its Application in
Cambodia. Unpublished manuscript, Dept. of Agriculture and Resource Economics, UC
Berkeley.
Gelbach, J. and Pritchett, L., 2002. Is More for the Poor Less for the Poor? The Politics of
Means-Tested Targeting. Topics in Economic Analysis and Policy, Vol 2(1).
Glewwe, P., 1992. Targeting Assistance to the Poor. Journal of Development Economics 38:
297-321.
Hentschel, J. and Lanjouw, P., 1996. Constructing an Indicator of Consumption for the
Analysis of Poverty: Principles and Illustrations with Reference to Ecuador. LSMS Working
Paper No.124, DECRG-World Bank: Washington DC.
31
Hentschel, J., Lanjouw, J.O., Lanjouw, P., and Poggi, J., 2000. Combining Census and Survey
Data to Trace the Spatial Dimensions of Poverty: A Case Study of Ecuador. World Bank
Economic Review 14(1)147-65.
Kanbur, R., 1987. Measurement and Alleviation of Poverty. IMF Staff Papers, 34:60-85.
Mistiaen, J., Özler, B., Razafimanantena, T., and Razafindravonona, J., 2002. Putting Welfare
on the Map in Madagascar. Africa Region Working Paper Series 34.
Ravallion, M. and Chao, K., 1988. Targeted Policies for Poverty Alleviation Under Imperfect
Information: Algorithms and Applications. Journal of Policy Modeling 11: 213-24.
Ravallion, M., 1993. Poverty Alleviation Through Regional Targeting: A Case Study of
Indonesia, in The Economics of Rural Organization: Theory, Practice and Policy, editors
Braverman, A., Hoff, K. and Stiglitz, J. World Bank and Oxford University Press.
32
Table 1. Data Summary
Ecuador
Madagascar
Cambodia
Year
1994
1993-4
1997
Source
Encuesta de
Condiciones de Vida
(ECV)
4,500 Households
Enquête Permanente Auprès
des Ménages (EPM)
Cambodia SocioEconomic Survey
(CSES)
6010
Hentschel and
Lanjouw (1996); and
Hentschel, Lanjouw,
Lanjouw and Poggi
(2000)
Mistiaen, Özler,
Razafimanantena and
Razafindravonona (2002)
Fujii (2003)
Year
1990
1993
1998
Coverage
About 10 million
individuals in 2 million
households
about 11.9 million
individuals
in 2.4 million households
About 11.0 million
individuals in
2.15 million households
Household Survey
Sample Size
References
4,508 Households
Population Census
33
Table 2a: Rural Ecuador
Impact on FGT2 of Targeting at Different Levels of Geographic Disaggregation
Optimal Targeting Scheme
Original FGT2
Uniform transfer
Region-level
targeting
(3 regions)
Province-level
targeting
(21 provinces)
Canton-level
targeting
(195 cantons)
Parroquia-level
targeting
(915 parroquias)
Low Budget
Low Poverty
Line
0.028
0.020
0.0188
High Budget
Low Poverty
Line
0.028
0.014
0.0128
Low Budget
High Poverty
Line
0.070
0.058
0.0565
High Budget
High Poverty
Line
0.070
0.047
0.0455
0.0184
0.0125
0.0557
0.0447
0.0177
0.0119
0.0544
0.0433
0.0167
0.0110
0.0528
0.0412
Table 2b: Urban and Rural Madagascar
Impact on FGT2 of Targeting at Different Levels of Geographic Disaggregation
Optimal Targeting Scheme
Low Budget
Low Poverty
Line
0.027
0.019
0.0154
Original FGT2
Uniform transfer
Province-level
targeting
(6 provinces)
Fivandrona-level 0.0138
targeting
(111
fivandronas)
Firaisana-level
0.0126
targeting
(1248 firaisanas)
High Budget
Low Poverty
Line
0.027
0.013
0.0097
Low Budget
High Poverty
Line
0.068
0.054
0.0500
High Budget
High Poverty
Line
0.068
0.043
0.0382
0.0080
0.0473
0.0346
0.0071
0.0455
0.0327
34
Table 2c: Urban and Rural Cambodia
Impact on FGT2 of Targeting at Different Levels of Geographic Disaggregation
Optimal Targeting Scheme
Original FGT2
Uniform transfer
Province-level
targeting
(44 urban plus
rural provinces)
District-level
targeting
(180 districts)
Commune-level
targeting
(1594
communes)
Low Budget
Low Poverty
Line
0.019
0.014
0.011
High Budget
Low Poverty
Line
0.019
0.009
0.007
Low Budget
High Poverty
Line
0.052
0.042
0.039
High Budget
High Poverty
Line
0.053
0.033
0.028
0.010
0.006
0.036
0.026
0.009
0.005
0.034
0.023
35
Table 3: Rural Ecuador
Testing for Statistical Significance of Differences in Poverty Estimates
Comparing Parroquia-Targeted Estimates Against Other Levels of Targeting
Parroquia-level
targeting FGT2
estimate
Uniform – parroquia
(s.e.)
Region – parroquia
(s.e)
Province - parroquia
(s.e.)
Canton – parroquia
(s.e)
Low Budget
Low Poverty
Line
0.0167
High Budget
Low Poverty
Line
0.0110
Low Budget
High Poverty
Line
0.0528
High Budget
High Poverty
Line
0.0412
0.0034
(0.00055)
0.0021
(0.00028)
0.0016
(0.00021)
0.00095
(0.00012)
0.0030
(0.00056)
0.0019
(0.00025)
0.0016
(0.00020)
0.00095
(0.00013)
0.0050
(0.0054)
0.0037
(0.00034)
0.0028
(0.00024)
0.0016
(0.00014)
0.0056
(0.00068)
0.0043
(0.00041)
0.0035
(0.00033)
0.0021
(0.00021)
36
Table 4a: Rural Ecuador
Cost of Achieving the Uniform Transfer Impact When Using Optimal Targeting
Expressed as a Percentage of Uniform Transfer Outlay
Uniform transfer
Region-level
targeting
(3 regions)
Province-level
targeting
(21 provinces)
Canton-level
targeting
(195 cantons)
Parroquia-level
targeting
(915 parroquias)
Low Budget
Low Poverty
Line
1.000
0.827
High Budget
Low Poverty
Line
1.000
0.881
Low Budget
High Poverty
Line
1.000
0.904
High Budget
High Poverty
Line
1.000
0.938
0.760
0.849
0.822
0.900
0.667
0.793
0.727
0.831
0.584
0.711
0.645
0.745
Table 4b: Urban and Rural Madagascar
Cost of Achieving the Uniform Transfer Impact When Using Optimal Targeting
Expressed as a Percentage of Uniform Transfer Outlay
Low Budget
Low Poverty
Line
1.000
0.607
Uniform transfer
Province-level
targeting
(6 provinces)
Fivandrona-level 0.464
targeting
(111
fivandronas)
Firaisana-level
0.376
targeting
(1248 firaisanas)
High Budget
Low Poverty
Line
1.000
0.699
Low Budget
High Poverty
Line
1.000
0.704
High Budget
High Poverty
Line
1.000
0.777
0.568
0.571
0.654
0.482
0.501
0.584
37
Table 4c: Urban and Rural Cambodia
Cost of Achieving the Uniform Transfer Impact When Using Optimal Targeting
Expressed as a Percentage of Uniform Transfer Outlay
Uniform transfer
Province-level
targeting
(44 urban plus
rural provinces)
District-level
targeting
(180 districts)
Commune-level
targeting
(1594
communes)
Low Budget
Low Poverty
Line
1.000
0.545
High Budget
Low Poverty
Line
1.000
0.696
Low Budget
High Poverty
Line
1.000
0.625
High Budget
High Poverty
Line
1.000
0.715
0.414
0.579
0.402
0.616
0.308
0.419
0.436
0.513
38
Table 5a: Rural Ecuador
Impact on FGT2 of Targeting at Different Levels of Geographic Disaggregation
“Naïve” Targeting Scheme
Original FGT2
Uniform transfer
Region-level
targeting
(3 regions)
Province-level
targeting
(21 provinces)
Canton-level
targeting
(195 cantons)
Parroquia-level
targeting
(915 parroquias)
Low Budget
Low Poverty
Line
0.028
0.020
0.0194
High Budget
Low Poverty
Line
0.028
0.014
0.0145
Low Budget
High Poverty
Line
0.070
0.058
0.0565
High Budget
High Poverty
Line
0.070
0.047
0.0456
0.0201
0.0188
0.0559
0.0457
0.0188
0.0172
0.0552
0.0446
0.0178
0.0162
0.0540
0.0425
Table 5b: Urban and Rural Madagascar
Impact on FGT2 of Targeting at Different Levels of Geographic Disaggregation
“Naïve” Targeting Scheme
Low Budget
Low Poverty
Line
0.027
0.019
0.0172
Original FGT2
Uniform transfer
Province-level
targeting
(6 provinces)
Fivandrona-level 0.0148
targeting
(111
fivandronas)
Firaisana-level
0.0135
targeting
(1248 firaisanas)
High Budget
Low Poverty
Line
0.027
0.013
0.0156
Low Budget
High Poverty
Line
0.068
0.054
0.0503
High Budget
High Poverty
Line
0.068
0.043
0.0383
0.0129
0.0489
0.0363
0.0114
0.0480
0.0348
39
Table 5c: Urban and Rural Cambodia
Impact on FGT2 of Targeting at Different Levels of Geographic Disaggregation
“Naïve” Targeting Scheme
Original FGT2
Uniform transfer
Province-level
targeting
(44 urban plus
rural provinces)
District-level
targeting
(180 districts)
Commune-level
targeting
(1594
communes)
Low Budget
Low Poverty
Line
0.019
0.014
0.012
High Budget
Low Poverty
Line
0.019
0.009
0.011
Low Budget
High Poverty
Line
0.052
0.042
0.039
High Budget
High Poverty
Line
0.053
0.033
0.030
0.011
0.010
0.038
0.028
0.010
0.008
0.036
0.026
40
Figure 1: Comparing Optimal and “Naïve Targeting in Madagascar with the Low Budget
and Low Poverty Line
Poverty Reduction using "Naïve" and Optimal Targeting Schemes in Madagascar
3.00
2.70
2.50
2.00
1.89
FGT2(*100)
1.72
1.54
1.50
1.48
1.38
1.35
1.26
naïve scheme
optimal scheme
1.00
0.50
0.00
original FGT2
uniform transfer
province-level
Fivondrona-level
Firaisana-level
targeting levels
Figure 2: Comparing Optimal and “Naïve Targeting in Madagascar with the High
Budget and Low Poverty Line
Poverty Reduction using "Naïve" and Optimal Targeting Schemes in Madagascar
3.00
2.70
2.50
FGT2 (*100)
2.00
1.56
1.50
naïve scheme
1.28
optimal scheme
1.29
1.14
0.97
1.00
0.80
0.71
0.50
0.00
original FGT2
uniform transfer
province-level
Fivondrona-level
Firaisana-level
targeting levels
41
Table 6: Distance between Optimal Geographic Targeting and “Perfect” Targeting in
Cambodia
% reduction in
% spent on nonpoor
FGT2
Budget= Total Poverty Gap for Low Poverty Line
FGT2 (*100)
Level of targeting
Pre-Transfer
Lump-sum Transfer
Province*Urban/Rural (44)
District (180)
Commune (1594)
Household (2130544)
Budget=Total
Pre-Transfer
Lump-sum Transfer
Province*Urban/Rural (44)
District (180)
Commune (1594)
Household (2130544)
1.93
1.47
81.2
1.23
71.2
1.12
67.0
0.99
62.5
0.00
0.00
Poverty Gap for High Poverty Line
5.23
2.80
64.1
2.31
56.0
2.13
53.1
1.80
49.4
0.00
0.00
23.9
36.4
41.9
48.7
100.0
46.42
55.86
59.31
64.12
100.0
42